Sentiment Analysis: Everything You Need to Know

What is Sentiment Analysis?

Types of Sentiment Analysis

Fine-grained Sentiment Analysis

  • Very positive
  • Positive
  • Neutral
  • Negative
  • Very negative
  • Very Positive = 5 stars
  • Very Negative = 1 star

Emotion Detection

Aspect-based Sentiment Analysis

Multilingual Sentiment Analysis

What makes Sentiment Analysis Important?

Benefits of sentiment analysis tools:

  • Sort Large Volumes of Data: Manually sorting massive volumes of data such as social media conversations, reviews, and surveys can be too time-consuming and inefficient. There is too much data to sort through and you will lose valuable time if you do it manually. With sentiment analysis, you can automate the process of analyzing large volumes of data efficiently and cost-effectively.
  • Analyze Data in Real-time: Sentiment analysis can help you analyze user opinions in real-time. You can automate the classification of customer support tickets based on issues or queries. You can also analyze social media conversations related to your brand and campaigns. You can quickly identify key issues and take action in real-time to resolve them.
  • Get a Consistent Criteria: People are frequently unable to assess the meaning of each piece of text with consistency. Humans have a success rate of 60–65 percent in determining the meaning of a text. Tagging text with sentiment is highly unreliable because it is subjective and influenced by personal experiences, thoughts, and beliefs. A centralized sentiment analysis system can benefit businesses by using the same data and criteria across all company-wide information, improving accuracy and analysis outcomes.

Understanding How Sentiment Analysis Works

  • Rule-based: These models perform sentiment analysis based on a pre-determined set of rules.
  • Automatic: These models leverage machine learning techniques to learn from data and increase accuracy.
  • Hybrid: The models combine both rule-based and automatic sentiment analysis approaches.

Rule-based Approach

  • Stemming, part-of-speech tagging (PoS), tokenization, parsing.
  • Lexicons (list of words such as emotions, expressions, etc)
  1. First, define lists of positive (bad, worst, poor, inferior) and negative words (good, great, best, excellent).
  2. Next, identify and count the number of positive and negative words from the text.
  3. If the number of positive words is more than the text has a positive sentiment. Similarly, if the number of negative words is more than the text carries negative sentiments.

Automatic Approach

Automatic Approach Training and Detection Method

Text Feature Extraction

Text Classification Algorithms:

  • Linear Regression: A statistical algorithm that predicts a variable (Y) based on a set of features (X).
  • Deep Learning: Utilizing artificial neural networks to analyze data to resemble the human brain with a varied array of methods
  • Naïve Bayes: A type of algorithm which employs Bayes’ Theorem to assign a text to a category
  • Support Vector Machines: A text-point-based model that does not use probability and instead represents each instance of text using multiple dimensions. The different opinions found in that section of the chart are mapped to different regions. Documents are also labeled based on associations with previous documents and physical locations.

Hybrid Approach

Challenges Faced When Building a Sentiment Analysis Model

Text Subjectivity and Tone

Text Polarity and Context

  • Unquestionably all of it.
  • Definitely nothing

Irony and Sarcasm in Text

Text Comparison

  • Nothing can beat this.
  • Older tools fall short of this.
  • Better than nothing.

Emojis in Text

  • Western Emojis: those encoded in one or two characters.
  • Eastern Emojis: Those with a longer combination of characters of a vertical nature.

Determining Neutral Sentiments

  • Objective-sounding texts do not contain any specific sentiments. You can tag such texts in the neutral category.
  • If you haven’t processed your data, it may contain irrelevant text which you can mark as neutral. However, only follow these instructions if you understand the ramifications for the entire project. The added noise can lead to a drop inaccuracy.
  • Generally, the text of the product contains impartial comments, such as the use of the phrase “I wish the software had more plugins.” Comparing products, such as saying I wish the software were better, is hard to classify.

Human Annotator Accuracy

Industry Applications of Sentiment Analysis

Brand Monitoring

Social Media Monitoring

Market Research

Customer Service

Voice of Customer (VOC)

The Best Sentiment Analysis Tools in 2021

Free Sentiment Analysis Tools

Sentiment Analysis Use Cases

General Sentiment Analysis

Product-related Sentiments

Hospitality Service Review Analysis

Twitter Data Analysis

Open Source VS Saas-Based Tools

Open Source Sentiment Analysis Tools




Sentiment Analysis Research and Studies

Sentiment Analysis Datasets

  • Restaurant reviews: 5.2 million Yelp reviews along with star ratings.
  • Fine-dining reviews: Amazon’s dataset has roughly 500,000 meal reviews. And each review has a plain text version as well as product and user information.
  • Product reviews: The dataset contains millions of Amazon customer reviews with star ratings, which is perfect for training sentiment analysis models.
  • Movie rating tweets: This dataset comprises 1,000 positive and 1,000 negative reviews. It also includes 5,331 positive and negative processed remarks and phrases.
  • Apple INC: This data set includes tweets about Apple Inc. It was gathered to examine user reactions about Apple INC.
  • Stock market-related tweets: This collection is made up of tweets sharing financial news. Of the Twitter messages that were studied, 3,685 were positive, and 2,106 were negative.
  • Although, if you are experimenting with rule-based sentiment analysis techniques, lists of lexicons can help you out. Here are a collection of lexicons (lists of words with labels indicating the sentiment they carry) that you can use to fuel your research and testing.
  • Sentiment Lexicons for 81 Languages: In this dataset, there are lexicons including both positive and negative sentiments in 81 languages.
  • SentiWordNet: With around 29,000 words, it contains sentiment scores ranging from 0 to 1.
  • Wordstat Sentiment Dictionary: Around 5000 positive and 9000 negative terms are found in this sample.
  • Opinion Lexicon for Sentiment Analysis: The dataset contains 4,782 English terms that are considered negative, and 2,005 words that are seen as positive.
  • Emoticon Sentiment Lexicon: A list of 477 emoticons, categorized as either positive, neutral, or negative, is in this dataset.

Sentiment Analysis Papers

Sentiment Analysis Courses

Sentiment Analysis Books

Wrapping Up



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